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Low-cost real-time aerial object detection and GPS location tracking pipeline

Real-time object detection and tracking is an active area of aerial remote sensing research that enables many environmental and ecological monitoring and preservation applications. Despite the development of several solutions tailored for these specific applications, trade-offs between cost efficiency and feature richness persist. This paper proposes a lightweight, low-cost, and modular approach to real-time object detection and instance tracking, enabling a wide gamut of use cases. By integrating real-time object detection models with affordable embedded hardware, we present a system that uses image metadata to perform geolocation on detected objects, enabling real-time applications due to minimal computational overhead. This algorithm generates cleaner ’areas of interest’ based on geolocated detections filtered by a clustering algorithm to remove false positives. In our findings, this proved a viable solution with real-time processing speeds and GPS positioning accuracy within a meter. While there is room for improvement, our proposed pipeline represents a significant step forward in lowering the costs involved with applying computer vision to conservation applications.

Ketersediaan
63621.3678Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
ISPRS Open Journal of Photogrammetry and Remote Sensing
No. Panggil
621.3678
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
8 hlm PDF, 7.920 KB
Bahasa
Inggris
ISBN/ISSN
1872-8235
Klasifikasi
621.3678
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.13, August 2024
Subjek
Machine Learning
Remote sensing
UAV
Real-time object detection
Computer vision
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Low-cost real-time aerial object detection and GPS location tracking pipeline
    Real-time object detection and tracking is an active area of aerial remote sensing research that enables many environmental and ecological monitoring and preservation applications. Despite the development of several solutions tailored for these specific applications, trade-offs between cost efficiency and feature richness persist. This paper proposes a lightweight, low-cost, and modular approach to real-time object detection and instance tracking, enabling a wide gamut of use cases. By integrating real-time object detection models with affordable embedded hardware, we present a system that uses image metadata to perform geolocation on detected objects, enabling real-time applications due to minimal computational overhead. This algorithm generates cleaner ’areas of interest’ based on geolocated detections filtered by a clustering algorithm to remove false positives. In our findings, this proved a viable solution with real-time processing speeds and GPS positioning accuracy within a meter. While there is room for improvement, our proposed pipeline represents a significant step forward in lowering the costs involved with applying computer vision to conservation applications.
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